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Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

Integrating project scheduling and material ordering problems is vital in realistically estimating project cost and duration. Also, the quality level of materials is important as one of the key project success factors.

In this paper, a three-objective mathematical model is presented for green project scheduling with materials ordering problems considering rental resources. The first objective is to minimize the total cost of the project site and logistics. The second objective is to minimize the environmental impacts of producing materials and the third objective is to maximize the total quality of materials. Since costs trigger several challenges in projects, cost constraints are considered in this model for the first time and also the cost of delay in supplying of materials by the suppliers has been deducted from the project costs. Subsequently, the model was implemented in a real case and solved by the Lagrangian Relaxation algorithm as an exact method on GAMS software for model validation.

Based on sensitivity analysis of some parameters, the findings indicate that the cost constraint and lead time have considerable effects on the project duration. Also, integrating project scheduling and material ordering improves the robustness of the project schedule.

The primary contributions of the present research can be stated as follows: considering the cost constraints in the project scheduling with material ordering problem, incorporating the rental resources and taking the quality levels of materials as well as the environmental impacts into account.

The purpose of this study was to address waste management in the food supply chain (FSC) through the integration of inspection processes in production and distribution centers under uncertain conditions, aiming to enhance sustainability across environmental, economic and social dimensions. The study introduces a sustainable forward and reverse FSC network using a closed-loop supply chain network approach to prevent the transfer of spoiled products, ultimately providing competitive advantages to stakeholders.

A robust multi-objective mathematical programming model is proposed, incorporating inspection processes to manage perishable products effectively. The model is solved using the Augmented Epsilon Constraint technique implemented in GAMS software, providing Pareto-optimal solutions tailored to decision-makers’ preferences. Furthermore, the methodology is applied in a real-world case study and solved with the Benders Decomposition algorithm to validate its practicality and effectiveness.

The proposed methodology effectively minimizes waste and enhances sustainability in the FSC by optimizing decision-making processes under uncertainty. The illustrative examples and real case study demonstrate the efficiency of the model and solution approach, highlighting the significant role of inspection in improving all three dimensions of sustainability.

The study offers valuable insights into and tools for food industry managers to make informed strategic and tactical decisions. By addressing waste management through advanced supply chain modeling, the research helps organizations reduce costs, improve sustainability and gain a competitive edge in the market.

This research is novel in its focus on integrating inspection into the FSC network and addressing uncertainty through robust mathematical modeling. It contributes to the existing literature by demonstrating the impact of inspection on sustainability in FSCs and providing practical solutions for real-world implementation.

The vehicle routing problem (VRP) is critical for the successful execution of logistics activities. However, there is strong evidence that efficiently solving the VRP is often complicated and requires more powerful – and possibly intelligent – support tools. In accordance with this necessity, the present study proposes a decision support system (DSS) applicable to the VRP, which includes both initial planning and replanning phases to support the real-time operations.

The proposed DSS lies at the basis of resilience thinking to provide a capacity to absorb and withstand the impact of disruptions, where resilience is connected with the factors of preparedness, flexibility and redundancy. These factors are approached in this study through a number of operational strategies in the reactive and proactive modes. The DSS includes a multi-layer perceptron neural network to predict changes that may arise in dynamic contexts, a modified k-means clustering algorithm to group customers with both static and dynamic attributes and two mixed-integer programming models to produce primary and alternate routing plans.

The research is motivated by the operational challenges faced by a collaborative networked clinical laboratory, which seeks to enhance efficiency and productivity in the daily management of medical sample collection and delivery through the implementation of increased automation. The findings reveal that centralized planning leads to heightened vulnerability in route planning and increased costs for replanning. Furthermore, the effectiveness of resilience-enhancement strategies varies based on the source and level of uncertainty.

The contributions of this paper are as follows: incorporating resilience thinking into the operational planning of logistics services, managing the decision-making of transport and collection companies through a DSS framework to ensure proper support to real-time operations, addressing the clustered VRP in a dynamic setting and adopting forecasting approaches to cover possible sources of dynamism.

Higher education institutions (HEIs) play a pivotal role in fostering economic development by cultivating skilled workforce and generating knowledge and innovation. However, HEIs may pose a potential risk to sustainable economic development due to the generation of food waste inside their campus canteens. Therefore, this study aims to examine the influence of attitude, subjective norm (SN), perceived behavior control (PBC), religiosity and pro-social behavior among State Islamic Religious College (SIRC) students on their intention to avoid food waste behavior. This study also focused on the mediating role of the three original theory of planned behavior (TPB) variables and pro-social behavior in the relationship between religiosity and the intention to reduce food waste.

Questionnaires were used to collect data from 443 students at SIRC. The collected data were processed and analyzed using structural equation modelling to test direct, indirect and mediating effects.

The empirical results indicated that the eagerness of students at SIRC to reduce their behavior of leaving food behind can be driven by their negative attitudes or views toward food waste, the practice of religious teachings in their lives, the belief that they can avoid food waste and their concern for the environment. The empirical results reveal that even though religiosity influences SN, it is unable to strengthen the relationship between religiosity and the desire to be anti-food waste.

In addition to contributing to the food waste literature in the context of eating behavior, the results of this study have theoretical and practical implications.

To assess SIRC students’ behavioral intentions to avoid food waste behavior, this study used a contemporary setting to measure attitude, SN, PBC, religiosity and pro-social behavior, so strengthening the TPB’s empirical underpinning.

This study empirically aims to examine the relationship between Bangladesh’s pharmaceutical industry growth and macroeconomic indicators such as the inflation rate, gross domestic product (GDP) growth, foreign direct investment (FDI) inflows, exchange rate and export growth through the long- and short-run relationship.

Using the time series data from 1986 to 2020, this study was developed based on the autoregressive distributed lag (ARDL) framework for co-integration. In contrast, the Toda–Yamamoto Granger Causality approach was also used for finding the direction of causality.

This study used the ARDL bounds test, which found strong co-integration among the variables, indicating a long-term relationship between them. In the long run, inflation, exchange rate and export growth significantly positively influence the pharmaceutical industry’s growth. Surprisingly, an FDI inflow has a negative impact. In the short term, the exchange rate and GDP growth were found to influence the growth of the pharmaceutical industry positively. Bidirectional causality between the growth of the pharmaceutical industry and the exchange rate was also identified using the Granger causality approach.

This paper emphasizes developing the policy as well as making concrete decisions regarding the development of the pharmaceutical industry and economic development in Bangladesh. The results also highlight the necessity for strategic macroeconomic management to support this sector’s long-term development and global competitiveness.

To the best of the authors’ knowledge, this paper is conducted to identify the short- and long-run relationship of pharmaceutical industry development with the economic indicators and progress, where no study has been found on this dimension.

Blockchain technology, renowned for its decentralization, security, reliability, and data integrity, has the potential to revolutionize businesses globally. However, its full potential remains unrealized due to adoption barriers, necessitating further studies to address these challenges. Identifying these barriers is crucial for businesses and practitioners to effectively tackle them. This systematic review analyzed 70 eligible studies out of 1944 gathered from various databases to understand and identify common blockchain adoption barriers. The Technology–Organization–Environment (TOE) framework was the most popular theory used in these studies. Despite differences in variable definitions, financial constraints, lack of stakeholder collaboration and coordination, and social influences like resistance to change and negative perceptions emerged as the top three barriers. The supply chain domain had the highest number of studies on blockchain adoption. Notably, there was a significant increase in studies addressing blockchain adoption in 2023, comprising 34.2% of the total reviewed studies. This review provides a comprehensive overview of identified barriers, serving as a valuable foundation for future research. Understanding these challenges allows researchers to design targeted studies aimed at developing solutions, strategies, and innovations to overcome obstacles hindering blockchain adoption.

The present study aims to identify the dimensions of destination psychological ownership (DPO) from tourists’ perspectives and to develop a reliable and valid measurement scale.

The mixed method has been applied in this study for the development of a scale to measure psychological ownership (PO) of tourists. The first stage includes identifying the PO dimensions of tourists toward the tourist destinations through in-depth interviews with experts (university professors, managers and experts in the tourism industry). Theme analysis was applied to analyze the data in this stage. A quantitative survey was conducted among tourists during the second stage. Accordingly, a questionnaire was designed and its reliability and validity were investigated. Confirmatory factor analysis and structural equation modeling were used to examine the structural model and its validity.

The findings revealed that the DPO from tourists’ perspectives includes six dimensions for the sense of attachment, responsibility, the sense of trust, the sense of honor, the sense of gratification and self-identity toward tourist destinations. The validity of the conceptual model was confirmed according to the results of the quantitative section of the study.

The present study is one of the limited numbers of studies coping with PO, especially in the field of tourism, from the view of the consumer. The present study identifies the dimensions of the DPO of tourists and develops an instrument to measure them. Therefore, the developed questionnaire can be used as a valid and reliable instrument to measure the DPO in future research.

Augmented reality (AR) is revolutionizing the tourism and hospitality industry by offering immersive experiences as well as creating more engaging, informative and accessible travel experiences that attract tourists from around the globe. From virtual tours and immersive historical site recreations to navigation assistance and cultural education, AR technology is transforming the way we explore and interact with the destinations. This study aims to identify benefits, risks, tools and techniques of AR in the tourism and hospitality literature.

The authors conducted a systematic literature review to answer six research questions. The authors also identified 33 primary studies, dated from January 2010 to February 2024 and coded them via a thematic analysis. Related studies were obtained through searching in Web of Science and Scopus.

The results identified nine themes for benefits, eight themes for risks/disadvantages and four tools and applications-related themes. Through the thematic analysis, the major benefits of AR in the tourism and hospitality were found to be differentiated travel experiences, improved performance of tourism value chain, more effective marketing efforts of tourism businesses, enhanced tourists’ engagement, enhanced performance of tourism destinations, stimulated behavioral intentions, tourist empowerment and providing more value, interactivity and integrity. Furthermore, eight risks were identified: physical, privacy and security, social, service failure, technical, psychological, managerial, information and knowledge gaps. The authors also recognized four tools and applications-related themes, namely, AR-enabled tools, AR applications, AR-enabled apps and AR-based techniques.

To the best of the authors’ knowledge, this review provides the first systematic exploration of the existing literature on usage of AR in the context of tourism and hospitality value chain.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.